Magnetic resonance imaging (MRI) plays a critical role in prostate cancer diagnosis, but is limited by variability in interpretation and diagnostic accuracy. This systematic review evaluates the current state of deep learning (DL) models in enhancing the automatic detection, localization, and characterization of clinically significant prostate cancer (csPCa) on MRI.
A systematic search was conducted across Medline/PubMed, Embase, Web of Science, and ScienceDirect for studies published between January 2020 and September 2023. Studies were included if these presented and validated fully automated DL models for csPCa detection on MRI, with pathology confirmation. Study quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool and the Checklist for Artificial Intelligence in Medical Imaging.
Twenty-five studies met the inclusion criteria, showing promising results in detecting and characterizing csPCa. However, significant heterogeneity in study designs, validation strategies, and datasets complicates direct comparisons. Only one-third of studies performed external validation, highlighting a critical gap in generalizability. The reliance on internal validation limits a broader application of these findings, and the lack of standardized methodologies hinders the integration of DL models into clinical practice.
DL models demonstrate significant potential in improving prostate cancer diagnostics on MRI. However, challenges in validation, generalizability, and clinical implementation must be addressed. Future research should focus on standardizing methodologies, ensuring external validation and conducting prospective clinical trials to facilitate the adoption of artificial intelligence (AI) in routine clinical settings. These findings support the cautious integration of AI into clinical practice, with further studies needed to confirm their efficacy in diverse clinical environments.
In this study, we reviewed how artificial intelligence (AI) models can help doctors better detect and understand aggressive prostate cancer using magnetic resonance imaging scans. We found that while these AI tools show promise, these tools need more testing and validation in different hospitals before these can be used widely in patient care.
European urology oncology. 2024 Nov 14 [Epub ahead of print]
Sébastien Molière, Dimitri Hamzaoui, Guillaume Ploussard, Romain Mathieu, Gaelle Fiard, Michael Baboudjian, Benjamin Granger, Morgan Roupret, Hervé Delingette, Raphaele Renard-Penna
Department of Radiology, Hôpital de Hautepierre, Hôpitaux Universitaire de Strasbourg, Strasbourg, France; Breast and Thyroid Imaging Unit, Institut de cancérologie Strasbourg Europe, Strasbourg, France; IGBMC, Illkirch, France. Electronic address: ., Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland., Department of Urology, La Croix du Sud Hôpital, Quint Fonsegrives, France; IUCT-O, Toulouse, France., Department of Urology, Inserm, EHESP, Irset (Institut de Recherche en Santé, Environnement et Travail), University of Rennes, Rennes, France., Department of Urology, CNRS, Grenoble INP, TIMC-IMAG, Grenoble Alpes University Hospital, Université Grenoble Alpes, Grenoble, France., Department of Urology, Assistance Publique des Hôpitaux de Marseille, Hôpital Nord, Marseille, France., Public Health Department, INSERM, IPLESP, AP-HP, Pitie-Salpetriere Hospital, Sorbonne Universite, Paris, France., Urology, GRC 5 Predictive Onco-Uro, AP-HP, Pitie-Salpetriere Hospital, Sorbonne University, Paris, France., Inria, Epione Team, Sophia Antipolis, Université Côte d'Azur, Nice, France., Department of Radiology, GRC 5 Predictive Onco-Uro, AP-HP, Pitie-Salpetriere Hospital, Sorbonne University, Paris, France.